'''ResNet in PyTorch.

For Pre-activation ResNet, see 'preact_resnet.py'.

Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
    Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
import copy

from witin_nn import WitinConv2d, WitinLinear, WitinBatchNorm2d

class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1, witin=False, layer_config=None):
        super(BasicBlock, self).__init__()
        self.witin = witin
        if self.witin:

            self.conv1 = WitinConv2d(
                in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False, 
                layer_config=copy.deepcopy(layer_config))
            self.bn1 = WitinBatchNorm2d(planes, layer_config=copy.deepcopy(layer_config))

            self.conv2 = WitinConv2d(
                planes, planes, kernel_size=3, stride=1, padding=1, bias=False, 
                layer_config=copy.deepcopy(layer_config))
            self.bn2 = WitinBatchNorm2d(planes, layer_config=copy.deepcopy(layer_config))

            self.shortcut = nn.Sequential()
            if stride != 1 or in_planes != self.expansion*planes:
                self.shortcut = nn.Sequential(
                    WitinConv2d(in_planes, self.expansion*planes,
                            kernel_size=1, stride=stride, bias=False, layer_config=copy.deepcopy(layer_config)),
                    WitinBatchNorm2d(self.expansion*planes, layer_config=copy.deepcopy(layer_config))
                )
        else:
            self.conv1 = nn.Conv2d(
                in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
            self.bn1 = nn.BatchNorm2d(planes)

            self.conv2 = nn.Conv2d(
                planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
            self.bn2 = nn.BatchNorm2d(planes)

            self.shortcut = nn.Sequential()
            if stride != 1 or in_planes != self.expansion*planes:
                self.shortcut = nn.Sequential(
                    nn.Conv2d(in_planes, self.expansion*planes,
                            kernel_size=1, stride=stride, bias=False),
                    nn.BatchNorm2d(self.expansion*planes)
                )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_planes, planes, stride=1, witin=False, layer_config=None):
        super(Bottleneck, self).__init__()
        if witin:

            self.conv1 = WitinConv2d(
                in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False, 
                layer_config=copy.deepcopy(layer_config))
            self.bn1 = WitinBatchNorm2d(planes, layer_config=copy.deepcopy(layer_config))

            self.conv2 = WitinConv2d(
                planes, planes, kernel_size=3, stride=stride, padding=1, bias=False, 
                layer_config=copy.deepcopy(layer_config))
            self.bn2 = WitinBatchNorm2d(planes, layer_config=copy.deepcopy(layer_config))
            
            self.conv3 = WitinConv2d(
                planes, self.expansion*planes, kernel_size=1, bias=False,
                layer_config=copy.deepcopy(layer_config))
            self.bn3 = WitinBatchNorm2d(planes, layer_config=copy.deepcopy(layer_config))

            self.shortcut = nn.Sequential()
            if stride != 1 or in_planes != self.expansion*planes:
                self.shortcut = nn.Sequential(
                    WitinConv2d(in_planes, self.expansion*planes,
                            kernel_size=1, stride=stride, bias=False, layer_config=copy.deepcopy(layer_config)),
                    WitinBatchNorm2d(self.expansion*planes, layer_config=copy.deepcopy(layer_config))
                )

        else:
            self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
            self.bn1 = nn.BatchNorm2d(planes)
            self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
                                stride=stride, padding=1, bias=False)
            self.bn2 = nn.BatchNorm2d(planes)

            self.conv3 = nn.Conv2d(planes, self.expansion *
                                planes, kernel_size=1, bias=False)
            self.bn3 = nn.BatchNorm2d(self.expansion*planes)

            self.shortcut = nn.Sequential()
            if stride != 1 or in_planes != self.expansion*planes:
                self.shortcut = nn.Sequential(
                    nn.Conv2d(in_planes, self.expansion*planes,
                            kernel_size=1, stride=stride, bias=False),
                    nn.BatchNorm2d(self.expansion*planes)
                )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out


class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10, witin=False, layer_config=None):
        super(ResNet, self).__init__()
        self.in_planes = 64
        self.witin = witin
        self.layer_config = layer_config
        if self.witin:

            self.conv1 = WitinConv2d(3, 64, kernel_size=3,
                        stride=1, padding=1, bias=False, layer_config=copy.deepcopy(layer_config))
            self.bn1 = WitinBatchNorm2d(64, layer_config=copy.deepcopy(layer_config))
            self.linear = WitinLinear(512*block.expansion, num_classes, 
                                      layer_config=copy.deepcopy(layer_config))
        else:
            self.conv1 = nn.Conv2d(3, 64, kernel_size=3,
                                stride=1, padding=1, bias=False)
            self.bn1 = nn.BatchNorm2d(64)
            self.linear = nn.Linear(512*block.expansion, num_classes)

        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)

        self.dropout = nn.Dropout(0.3)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            if self.witin:
                layers.append(block(self.in_planes, planes, stride, self.witin, self.layer_config))
            else:
                layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        out = self.dropout(out)
        out = self.linear(out)
        return out


def ResNet18(witin=False, layer_config=None):
    return ResNet(BasicBlock, [2, 2, 2, 2], witin=witin, layer_config=layer_config)


def ResNet34():
    return ResNet(BasicBlock, [3, 4, 6, 3])


def ResNet50():
    return ResNet(Bottleneck, [3, 4, 6, 3])


def ResNet101():
    return ResNet(Bottleneck, [3, 4, 23, 3])


def ResNet152():
    return ResNet(Bottleneck, [3, 8, 36, 3])


def test():
    net = ResNet18()
    y = net(torch.randn(1, 3, 32, 32))
    print(y.size())

# test()
